Operator-ready prompt for reuse, tuning, and workspace runs.
This item is set up for developers who want to inspect the original language, fork it into Workspace, and adapt the evidence model without losing the source prompt structure.
Implementation handoffs, eval setup, and prompt tuning where you need the original structure intact.
Inspect first, copy once, then fork into Workspace when you want variants, notes, and model settings attached to the same run.
Swap domain facts, examples, and any hard-coded entities for your own context.
Tighten the evidence or verification requirement if this is headed toward production.
Decide which failure mode you want to evaluate first before you branch the prompt.
This prompt already carries implementation detail, tool context, and a final-output instruction. Keep that structure intact when you tune it, or your comparison runs get noisy fast.
Open this prompt inside Workspace when you want a live iteration loop.
Copy for quick reuse, or run it in Workspace to keep prompt variants, model settings, and prompt-history changes in one place.
Structured source with 1 active lines to adapt.
Already linked to a challenge workflow.
Sign in to keep private prompt variations.
Prompt content
Original prompt text with formatting preserved for inspection and clean copy.
Design a CrewAI multi-agent system for financial risk assessment. Define at least three distinct agent roles (e.g., Data Gatherer, Financial Analyst, Risk Strategist), their specific tasks, and how they will collaborate. Detail the communication flow and the artifacts they exchange. How will the system use MCP for tool integration and RAG for document processing?
Adaptation plan
Keep the source stable, then branch your edits in a predictable order so the next prompt run is easier to evaluate.
Preserve the role framing, objective, and reporting structure so comparison runs stay coherent.
Swap in your own domain constraints, anomaly thresholds, and examples before you branch variants.
Check whether the prompt asks for the right evidence, confidence signal, and escalation path.
Copy once for a pristine source snapshot, then move the prompt into Workspace when you want variants, run history, and side-by-side tuning without losing the original.
Prompt diagnostics
Quick signals for how structured this prompt already is and where adaptation work is likely to happen first.
This prompt is mostly narrative and instruction-driven, so you can adapt examples and output constraints first without disturbing the structure.
MCP-Enabled AI Investment Risk Agent with Claude Opus 4.1 & CrewAI
The rapid influx of capital into AI infrastructure companies, totaling over $100B in 2025, presents a complex investment landscape. Small, unproven AI businesses face significantly higher interest rates due to investor skepticism. This challenge focuses on building an advanced multi-agent system to automate the financial risk assessment for such nascent AI companies. You will orchestrate a team of specialized agents using CrewAI, powered by Claude Opus 4.1, to conduct deep financial due diligence. The system will leverage RAG to process investor decks and public financial statements, and critically, integrate external financial data via MCP tool integration, enabling real-time access to market benchmarks and credit ratings. The goal is to provide a comprehensive risk report, identifying key financial vulnerabilities and opportunities for AI startups seeking investment.
Use the challenge page to recover the original task boundaries before you tune the prompt. That keeps your variants grounded in the same evaluation target instead of drifting into a different problem.